AI Modules
AI Modules are the intelligence layer of every Ignitia protocol.
These aren’t optional plugins, they’re core logic components that define how your protocol senses, reacts, and evolves. You can select one or more AI modules during the build process, and each one injects its own smart behavior directly into your contract.
How AI Modules Work
Each module:
Adds a self-contained logic unit to your protocol
Generates one or more smart contract functions
Can be customized via a natural language description
Connects to on-chain or off-chain data feeds
Is deployable on its own or combined with others
The more specific your description, the smarter the logic.
Available Modules
Here’s what’s currently supported (click-through pages recommended for deeper docs later):
🧠 Predictive Signals
Use case: Forecast future protocol states (like user churn or volume) and adapt logic accordingly.
Adjust emission rates based on projected demand
Rebalance staking multipliers based on market volatility
Example function:
adjust_rewards_based_on_forecast()
🛡️ Anomaly Detection
Use case: Monitor for abnormal activity and take automatic defensive action.
Pause protocol on volume spikes or liquidity drains
Emit alerts or restrict transactions temporarily
Example function:
pause_on_liquidity_spike()
🗳️ Automated Governance
Use case: Let the protocol self-propose and adapt its rules over time.
Auto-initiate proposals based on treasury state
Restrict malicious proposal spam via agent filters
Example function:
propose_new_fee_structure()
💰 AI Treasury Management
Use case: Optimize inflows, outflows, and reserves using live signals.
Rebalance portfolio based on market data
Trigger token buybacks when revenues exceed burn threshold
Example function:
rebalance_vault_assets()
🔁 Dynamic Incentives
Use case: Adjust incentives in real time to maintain protocol health.
Modify staking yields based on user activity
Reduce rewards in times of high inflation
Example function:
adjust_incentive_curve()
✍️ Custom AI Agent
Use case: Write your own prompt to create a fully custom logic module.
Define a novel agent behavior (e.g., “run loyalty points logic based on DAO votes”)
Great for experimental protocols or edge-case strategies
Fully sandboxed and isolated from core logic
Composability
Modules are:
Composable — run independently or in tandem
Upgradeable — optionally governed post-deploy
Auditable — each module is inspected before launch via AI Feedback
You can mix-and-match to create intelligent, adaptive protocols that respond to real-world data and evolving on-chain dynamics.
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